forked from eval-protocol/python-sdk
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest_batch_evaluation.py
More file actions
1202 lines (1028 loc) · 45.8 KB
/
Copy pathtest_batch_evaluation.py
File metadata and controls
1202 lines (1028 loc) · 45.8 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
"""
End-to-end integration tests for batch evaluation feature.
These tests validate the entire batch evaluation pipeline with live API calls
to both Fireworks and OpenAI, ensuring production readiness.
"""
import asyncio
import json
import logging
import os
import tempfile
from pathlib import Path
from typing import Any, Dict
from unittest.mock import AsyncMock, Mock, patch
import pytest
from eval_protocol.agent.task_manager import TaskManager
from eval_protocol.cli_commands.agent_eval_cmd import agent_eval_command
from eval_protocol.models import TaskDefinitionModel
class MockArgs:
"""Mock args object for agent_eval_command."""
def __init__(self, task_def: str, num_rollouts: int = 2, **kwargs):
self.task_def = task_def
self.num_rollouts = num_rollouts
self.parallel = kwargs.get("parallel", False)
self.max_concurrency = kwargs.get("max_concurrency", 3)
self.model = kwargs.get("model", None)
self.filter = kwargs.get("filter", None)
class TestBatchEvaluation:
"""Integration tests for batch evaluation functionality."""
def _create_sophisticated_game_mock(self):
"""Create the sophisticated FrozenLakeGameMock for realistic game simulation."""
class FrozenLakeGameMock:
def __init__(self):
self.episodes = {}
self.call_count = 0
def handle_request(self, *args, **kwargs):
"""Handle HTTP requests and simulate Frozen Lake game logic"""
self.call_count += 1
# Parse the request to determine the endpoint
if hasattr(args[0], "endswith"):
url = args[0]
elif "url" in kwargs:
url = kwargs["url"]
else:
url = str(args[0]) if args else ""
# Check if this is a step request with JSON data
json_data = kwargs.get("json", {})
if "/start_episode" in url:
return self._start_episode()
elif "/step" in url and json_data:
episode_id = json_data.get("episode_id")
action = json_data.get("action")
return self._step(episode_id, action)
else:
# Default response for other requests
return self._default_response()
def _start_episode(self):
"""Start a new episode"""
episode_id = f"episode_{self.call_count}"
self.episodes[episode_id] = {
"position": [0, 0], # Start position
"step_count": 0,
"done": False,
"won": False,
"grid": [
["S", "F", "F", "F"],
["F", "H", "F", "H"],
["F", "F", "F", "H"],
["H", "F", "F", "G"],
],
}
observation = {
"position": [0, 0],
"current_cell": "S",
"done": False,
"won": False,
"message": "Game started. You are at the starting position.",
"visual": self._generate_visual(episode_id),
"step_count": 0,
}
response = Mock()
response.status_code = 200
response.json.return_value = {
"episode_id": episode_id,
"observation": observation,
}
return response
def _step(self, episode_id, action):
"""Process a step action"""
if episode_id not in self.episodes:
# Episode doesn't exist, create a basic one
self.episodes[episode_id] = {
"position": [0, 0],
"step_count": 0,
"done": False,
"won": False,
"grid": [
["S", "F", "F", "F"],
["F", "H", "F", "H"],
["F", "F", "F", "H"],
["H", "F", "F", "G"],
],
}
episode = self.episodes[episode_id]
if episode["done"]:
# Episode already finished
observation = self._get_observation(episode_id)
else:
# Process the action
episode["step_count"] += 1
old_pos = episode["position"].copy()
new_pos = self._apply_action(episode["position"], action)
episode["position"] = new_pos
# Check what happened
row, col = new_pos
cell = episode["grid"][row][col]
if cell == "G":
# Reached goal!
episode["done"] = True
episode["won"] = True
message = "Congratulations! You reached the goal! You win! Success!"
elif cell == "H":
# Fell in hole
episode["done"] = True
episode["won"] = False
message = "Oh no! You fell into a hole. Game over."
else:
# Normal move
action_names = {0: "left", 1: "down", 2: "right", 3: "up"}
action_name = action_names.get(action, "unknown")
if new_pos != old_pos:
message = f"You moved {action_name} to a {cell} cell."
else:
message = f"You tried to move {action_name} but hit a wall."
observation = {
"position": new_pos,
"current_cell": cell,
"done": episode["done"],
"won": episode["won"],
"message": message,
"visual": self._generate_visual(episode_id),
"step_count": episode["step_count"],
}
response = Mock()
response.status_code = 200
response.json.return_value = {
"observation": observation,
"is_done": episode["done"],
"info": {"step_count": episode["step_count"]},
}
return response
def _apply_action(self, position, action):
"""Apply action and return new position"""
row, col = position
# Action mapping: 0=left, 1=down, 2=right, 3=up
if action == 0: # left
new_col = max(0, col - 1)
return [row, new_col]
elif action == 1: # down
new_row = min(3, row + 1)
return [new_row, col]
elif action == 2: # right
new_col = min(3, col + 1)
return [row, new_col]
elif action == 3: # up
new_row = max(0, row - 1)
return [new_row, col]
else:
return position # Invalid action, stay in place
def _generate_visual(self, episode_id):
"""Generate visual representation of the game"""
episode = self.episodes[episode_id]
grid = episode["grid"]
pos = episode["position"]
visual_lines = []
for r in range(4):
line = ""
for c in range(4):
if [r, c] == pos:
line += f"[{grid[r][c]}] "
else:
line += f" {grid[r][c]} "
visual_lines.append(line.rstrip())
return "\n".join(visual_lines)
def _get_observation(self, episode_id):
"""Get current observation for an episode"""
episode = self.episodes[episode_id]
row, col = episode["position"]
cell = episode["grid"][row][col]
return {
"position": episode["position"],
"current_cell": cell,
"done": episode["done"],
"won": episode["won"],
"message": (
"Congratulations! You reached the goal! You win! Success!"
if episode["won"]
else "Game finished."
),
"visual": self._generate_visual(episode_id),
"step_count": episode["step_count"],
}
def _default_response(self):
"""Default response for unhandled requests"""
response = Mock()
response.status_code = 200
response.json.return_value = {"status": "ok"}
return response
return FrozenLakeGameMock()
def setup_method(self):
"""Set up test environment before each test."""
# Ensure we have the necessary environment variables
self.original_env = {}
# Store original environment values
env_vars = ["FIREWORKS_API_KEY", "OPENAI_API_KEY", "MODEL_AGENT"]
for var in env_vars:
self.original_env[var] = os.environ.get(var)
# Set default model for agent if not specified
if not os.environ.get("MODEL_AGENT"):
os.environ["MODEL_AGENT"] = "accounts/fireworks/models/qwen3-235b-a22b"
# Set mock API keys to avoid skipping tests
os.environ["FIREWORKS_API_KEY"] = "mock-fireworks-key"
os.environ["OPENAI_API_KEY"] = "mock-openai-key"
def teardown_method(self):
"""Clean up after each test."""
# Restore original environment
for var, value in self.original_env.items():
if value is None:
os.environ.pop(var, None)
else:
os.environ[var] = value
@pytest.mark.asyncio
@patch("eval_protocol.agent.orchestrator.AsyncOpenAI")
@patch("subprocess.Popen")
@patch("aiohttp.ClientSession.post")
@patch("httpx.Client.post")
@patch("requests.get")
async def test_batch_evaluation_task_manager_fireworks(
self,
mock_requests_get,
mock_httpx_post,
mock_aiohttp_post,
mock_subprocess_popen,
mock_openai_constructor,
):
"""Test batch evaluation using TaskManager with Fireworks API."""
# Mock OpenAI client in orchestrator
mock_openai_client = AsyncMock()
mock_openai_constructor.return_value = mock_openai_client
# Mock OpenAI completion response - simulate smart AI moves
def create_tool_call_response(action, call_id):
mock_tool_call = Mock()
mock_tool_call.function.name = "step"
mock_tool_call.function.arguments = f'{{"action": "{action}"}}'
mock_tool_call.id = call_id
mock_message = Mock()
mock_message.content = None
mock_message.role = "assistant"
mock_message.tool_calls = [mock_tool_call]
mock_message.model_dump = Mock(
return_value={
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": call_id,
"type": "function",
"function": {
"name": "step",
"arguments": f'{{"action": "{action}"}}',
},
}
],
}
)
mock_completion = AsyncMock()
mock_completion.choices = [Mock(message=mock_message)]
mock_completion.usage = Mock(total_tokens=10)
return mock_completion
# Smart AI with winning sequence: right -> right -> down -> down -> down -> right
winning_sequence = ["right", "right", "down", "down", "down", "right"]
rollout_counter = [0]
def smart_move_generator(**kwargs):
messages = kwargs.get("messages", [])
move_count = sum(1 for msg in messages if msg.get("role") == "assistant" and msg.get("tool_calls"))
rollout_counter[0] += 1
if rollout_counter[0] <= 6: # First rollout wins
action = winning_sequence[move_count] if move_count < len(winning_sequence) else "right"
else: # Second rollout makes mistake
action = (
"right"
if move_count == 4
else (winning_sequence[move_count] if move_count < len(winning_sequence) else "right")
)
return create_tool_call_response(action, f"call_{move_count}")
mock_openai_client.chat.completions.create = AsyncMock(side_effect=smart_move_generator)
# Mock Fireworks API response (backup)
mock_response = AsyncMock()
mock_response.status = 200
mock_response.json = AsyncMock(
return_value={
"choices": [
{
"message": {"content": "right", "role": "assistant"},
"finish_reason": "stop",
}
],
"usage": {"total_tokens": 10},
}
)
mock_aiohttp_post.return_value.__aenter__.return_value = mock_response
# Use sophisticated game mock
game_mock = self._create_sophisticated_game_mock()
mock_httpx_post.side_effect = game_mock.handle_request
# Mock health check
mock_health_response = Mock()
mock_health_response.status_code = 200
mock_requests_get.return_value = mock_health_response
task_manager = TaskManager()
# Load the frozen lake task definition for Fireworks
task_def_path = Path("examples/frozen_lake/client/task_def.yaml")
if not task_def_path.exists():
pytest.skip(f"Task definition not found: {task_def_path}")
# Load and register the task
task_def = task_manager._load_task_from_file(str(task_def_path))
assert task_def is not None, "Failed to load task definition"
# Override for traditional batch evaluation (not data-driven)
task_def.dataset_path = None # Remove dataset path to use traditional evaluation
task_def.num_rollouts = 2
task_id = task_manager.register_task(task_def)
assert task_id == "frozen_lake_http_rollout"
# Configure subprocess.Popen mock to prevent real process creation
mock_process = Mock()
mock_process.pid = 12345
mock_process.poll.return_value = None
mock_process.communicate.return_value = (b"", b"")
mock_subprocess_popen.return_value = mock_process
try:
# Mock server process management
with (
patch.object(task_manager, "_start_resource_server", return_value=12345),
patch.object(task_manager, "_wait_for_server_health", return_value=True),
):
# Execute the task with batch evaluation
results = await task_manager.execute_tasks(
task_ids=[task_id],
parallel=False,
max_concurrency=2,
num_rollouts_override=2,
)
# Validate results structure
assert task_id in results
result = results[task_id]
# Should not be an error result
assert not (isinstance(result, dict) and "error" in result), (
f"Task failed: {result.get('error', 'Unknown error')}"
)
# Should be aggregated results
assert isinstance(result, dict)
assert result.get("aggregated", False), "Results should be aggregated for batch evaluation"
# Validate aggregated result structure
required_keys = [
"num_rollouts",
"successful_rollouts",
"success_rate",
"avg_score",
"min_score",
"max_score",
]
for key in required_keys:
assert key in result, f"Missing key in aggregated results: {key}"
# Validate result values
assert result["num_rollouts"] == 2
assert result["successful_rollouts"] >= 0
assert result["successful_rollouts"] <= result["num_rollouts"]
assert 0.0 <= result["success_rate"] <= 1.0
assert isinstance(result["avg_score"], (int, float))
assert isinstance(result["min_score"], (int, float))
assert isinstance(result["max_score"], (int, float))
assert result["min_score"] <= result["avg_score"] <= result["max_score"]
# Should have individual results
assert "individual_scores" in result
assert "individual_results" in result
assert len(result["individual_scores"]) == result["successful_rollouts"]
assert len(result["individual_results"]) == result["successful_rollouts"]
logging.info(f"Fireworks batch evaluation completed successfully: {result}")
finally:
await task_manager.cleanup()
@pytest.mark.asyncio
@patch("eval_protocol.agent.orchestrator.AsyncOpenAI")
@patch("subprocess.Popen")
@patch("httpx.Client.post")
@patch("requests.get")
async def test_batch_evaluation_task_manager_openai(
self, mock_requests_get, mock_httpx_post, mock_subprocess_popen, mock_openai
):
"""Test batch evaluation using TaskManager with OpenAI API."""
# Mock OpenAI client and response
mock_openai_client = AsyncMock()
mock_openai.return_value = mock_openai_client
mock_completion = AsyncMock()
# Mock OpenAI completion response with proper tool call structure
mock_tool_call = Mock()
mock_tool_call.function.name = "step"
mock_tool_call.function.arguments = '{"action": "down"}'
mock_tool_call.id = "call_openai_123"
mock_message = Mock()
mock_message.content = None
mock_message.role = "assistant"
mock_message.tool_calls = [mock_tool_call]
mock_message.model_dump = Mock(
return_value={
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call_openai_123",
"type": "function",
"function": {"name": "step", "arguments": '{"action": "down"}'},
}
],
}
)
mock_completion = AsyncMock()
mock_completion.choices = [Mock(message=mock_message)]
mock_completion.usage = Mock(total_tokens=15)
mock_openai_client.chat.completions.create = AsyncMock(return_value=mock_completion)
# Mock HTTP rollout server responses
mock_httpx_response = Mock()
mock_httpx_response.status_code = 200
# Create responses for OpenAI test
responses = [
{"episode_id": "test_episode_openai"},
{
"position": [1, 0],
"current_cell": "F",
"done": False,
"won": False,
"message": "You moved down",
"visual": " S F F F\n[F] H F H\n F F F H\n H F F G",
},
{
"position": [3, 3],
"current_cell": "G",
"done": True,
"won": True,
"message": "Victory!",
"visual": " S F F F\n F H F H\n F F F H\n H F F [G]",
},
]
response_iter = iter(responses * 10)
mock_httpx_response.json.side_effect = lambda: next(response_iter, responses[-1])
mock_httpx_post.return_value = mock_httpx_response
# Mock health check
mock_health_response = Mock()
mock_health_response.status_code = 200
mock_requests_get.return_value = mock_health_response
task_manager = TaskManager()
# Load the frozen lake task definition for OpenAI
task_def_path = Path("examples/frozen_lake/client/task_def_openai.yaml")
if not task_def_path.exists():
pytest.skip(f"Task definition not found: {task_def_path}")
# Load and register the task
task_def = task_manager._load_task_from_file(str(task_def_path))
assert task_def is not None, "Failed to load task definition"
# Override num_rollouts to reduce test time
task_def.num_rollouts = 2
# Set OpenAI model temporarily
original_model = os.environ.get("MODEL_AGENT")
os.environ["MODEL_AGENT"] = "gpt-4o-mini"
task_id = task_manager.register_task(task_def)
assert task_id == "frozen_lake_http_rollout_openai"
# Configure subprocess.Popen mock to prevent real process creation
mock_process = Mock()
mock_process.pid = 12346
mock_process.poll.return_value = None
mock_process.communicate.return_value = (b"", b"")
mock_subprocess_popen.return_value = mock_process
try:
# Mock server process management
with (
patch.object(task_manager, "_start_resource_server", return_value=12346),
patch.object(task_manager, "_wait_for_server_health", return_value=True),
):
# Execute the task with batch evaluation
results = await task_manager.execute_tasks(
task_ids=[task_id],
parallel=False,
max_concurrency=2,
num_rollouts_override=2,
)
# Validate results structure
assert task_id in results
result = results[task_id]
# Should not be an error result
assert not (isinstance(result, dict) and "error" in result), (
f"Task failed: {result.get('error', 'Unknown error')}"
)
# Should be aggregated results
assert isinstance(result, dict)
assert result.get("aggregated", False), "Results should be aggregated for batch evaluation"
# Validate aggregated result structure
required_keys = [
"num_rollouts",
"successful_rollouts",
"success_rate",
"avg_score",
"min_score",
"max_score",
]
for key in required_keys:
assert key in result, f"Missing key in aggregated results: {key}"
# Validate result values
assert result["num_rollouts"] == 2
assert result["successful_rollouts"] >= 0
assert result["successful_rollouts"] <= result["num_rollouts"]
assert 0.0 <= result["success_rate"] <= 1.0
assert isinstance(result["avg_score"], (int, float))
assert isinstance(result["min_score"], (int, float))
assert isinstance(result["max_score"], (int, float))
assert result["min_score"] <= result["avg_score"] <= result["max_score"]
# Should have individual results
assert "individual_scores" in result
assert "individual_results" in result
assert len(result["individual_scores"]) == result["successful_rollouts"]
assert len(result["individual_results"]) == result["successful_rollouts"]
logging.info(f"OpenAI batch evaluation completed successfully: {result}")
finally:
# Restore original model
if original_model:
os.environ["MODEL_AGENT"] = original_model
else:
os.environ.pop("MODEL_AGENT", None)
await task_manager.cleanup()
@patch("eval_protocol.agent.orchestrator.AsyncOpenAI")
@patch("subprocess.Popen")
@patch("aiohttp.ClientSession.post")
@patch("httpx.Client.post")
@patch("requests.get")
def test_cli_batch_evaluation_fireworks(
self,
mock_requests_get,
mock_httpx_post,
mock_aiohttp_post,
mock_subprocess_popen,
mock_openai_constructor,
):
"""Test batch evaluation through CLI command with Fireworks."""
# Mock OpenAI client in orchestrator
mock_openai_client = AsyncMock()
mock_openai_constructor.return_value = mock_openai_client
mock_completion = AsyncMock()
# Mock OpenAI completion response with proper tool call structure
mock_tool_call = Mock()
mock_tool_call.function.name = "step"
mock_tool_call.function.arguments = '{"action": "up"}'
mock_tool_call.id = "call_cli_fw"
mock_message = Mock()
mock_message.content = None
mock_message.role = "assistant"
mock_message.tool_calls = [mock_tool_call]
mock_message.model_dump = Mock(
return_value={
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call_cli_fw",
"type": "function",
"function": {"name": "step", "arguments": '{"action": "up"}'},
}
],
}
)
mock_completion = AsyncMock()
mock_completion.choices = [Mock(message=mock_message)]
mock_completion.usage = Mock(total_tokens=8)
mock_openai_client.chat.completions.create = AsyncMock(return_value=mock_completion)
# Mock Fireworks API response (backup)
mock_response = AsyncMock()
mock_response.status = 200
mock_response.json = AsyncMock(
return_value={
"choices": [
{
"message": {"content": "up", "role": "assistant"},
"finish_reason": "stop",
}
],
"usage": {"total_tokens": 8},
}
)
mock_aiohttp_post.return_value.__aenter__.return_value = mock_response
# Mock HTTP rollout responses
mock_httpx_response = Mock()
mock_httpx_response.status_code = 200
responses = [
{"episode_id": "test_cli_fw"},
{"position": [0, 0], "done": False, "won": False, "message": "Test CLI FW"},
{"position": [3, 3], "done": True, "won": True, "message": "CLI Win!"},
]
response_iter = iter(responses * 5)
mock_httpx_response.json.side_effect = lambda: next(response_iter, responses[-1])
mock_httpx_post.return_value = mock_httpx_response
# Mock health check
mock_health_response = Mock()
mock_health_response.status_code = 200
mock_requests_get.return_value = mock_health_response
task_def_path = Path("examples/frozen_lake/client/task_def.yaml")
if not task_def_path.exists():
pytest.skip(f"Task definition not found: {task_def_path}")
# Create mock args for CLI command
args = MockArgs(
task_def=str(task_def_path),
num_rollouts=2,
parallel=False,
max_concurrency=2,
)
# Configure subprocess.Popen mock to prevent real process creation
mock_process = Mock()
mock_process.pid = 12348
mock_process.poll.return_value = None
mock_process.communicate.return_value = (b"", b"")
mock_subprocess_popen.return_value = mock_process
# Execute CLI command with mocked subprocess for server management
with patch("time.sleep"):
exit_code = agent_eval_command(args)
# Should complete successfully
assert exit_code == 0, "CLI command should complete successfully"
@patch("eval_protocol.agent.orchestrator.AsyncOpenAI")
@patch("subprocess.Popen")
@patch("httpx.Client.post")
@patch("requests.get")
def test_cli_batch_evaluation_openai(self, mock_requests_get, mock_httpx_post, mock_subprocess_popen, mock_openai):
"""Test batch evaluation through CLI command with OpenAI."""
# Mock OpenAI client
mock_openai_client = AsyncMock()
mock_openai.return_value = mock_openai_client
mock_completion = AsyncMock()
# Mock OpenAI completion response with proper tool call structure
mock_tool_call = Mock()
mock_tool_call.function.name = "step"
mock_tool_call.function.arguments = '{"action": "left"}'
mock_tool_call.id = "call_cli_openai"
mock_message = Mock()
mock_message.content = None
mock_message.role = "assistant"
mock_message.tool_calls = [mock_tool_call]
mock_message.model_dump = Mock(
return_value={
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call_cli_openai",
"type": "function",
"function": {"name": "step", "arguments": '{"action": "left"}'},
}
],
}
)
mock_completion = AsyncMock()
mock_completion.choices = [Mock(message=mock_message)]
mock_completion.usage = Mock(total_tokens=10)
mock_openai_client.chat.completions.create = AsyncMock(return_value=mock_completion)
# Mock HTTP rollout responses
mock_httpx_response = Mock()
mock_httpx_response.status_code = 200
responses = [
{"episode_id": "test_cli_openai"},
{
"position": [0, 0],
"done": False,
"won": False,
"message": "Test CLI OpenAI",
},
{
"position": [3, 3],
"done": True,
"won": True,
"message": "CLI OpenAI Win!",
},
]
response_iter = iter(responses * 5)
mock_httpx_response.json.side_effect = lambda: next(response_iter, responses[-1])
mock_httpx_post.return_value = mock_httpx_response
# Mock health check
mock_health_response = Mock()
mock_health_response.status_code = 200
mock_requests_get.return_value = mock_health_response
task_def_path = Path("examples/frozen_lake/client/task_def_openai.yaml")
if not task_def_path.exists():
pytest.skip(f"Task definition not found: {task_def_path}")
# Set OpenAI model
original_model = os.environ.get("MODEL_AGENT")
os.environ["MODEL_AGENT"] = "gpt-4o-mini"
try:
# Create mock args for CLI command
args = MockArgs(
task_def=str(task_def_path),
num_rollouts=2,
parallel=False,
max_concurrency=2,
)
# Configure subprocess.Popen mock to prevent real process creation
mock_process = Mock()
mock_process.pid = 12349
mock_process.poll.return_value = None
mock_process.communicate.return_value = (b"", b"")
mock_subprocess_popen.return_value = mock_process
# Execute CLI command with mocked subprocess for server management
with patch("time.sleep"):
exit_code = agent_eval_command(args)
# Should complete successfully
assert exit_code == 0, "CLI command should complete successfully"
finally:
# Restore original model
if original_model:
os.environ["MODEL_AGENT"] = original_model
else:
os.environ.pop("MODEL_AGENT", None)
@pytest.mark.asyncio
@patch("eval_protocol.agent.orchestrator.AsyncOpenAI")
@patch("subprocess.Popen")
@patch("aiohttp.ClientSession.post")
@patch("httpx.Client.post")
@patch("requests.get")
async def test_parallel_batch_evaluation(
self,
mock_requests_get,
mock_httpx_post,
mock_aiohttp_post,
mock_subprocess_popen,
mock_openai_constructor,
):
"""Test parallel execution of multiple rollouts."""
# Mock OpenAI client in orchestrator
mock_openai_client = AsyncMock()
mock_openai_constructor.return_value = mock_openai_client
# Mock OpenAI completion response with smart AI moves
def create_tool_call_response(action, call_id):
mock_tool_call = Mock()
mock_tool_call.function.name = "step"
mock_tool_call.function.arguments = f'{{"action": "{action}"}}'
mock_tool_call.id = call_id
mock_message = Mock()
mock_message.content = None
mock_message.role = "assistant"
mock_message.tool_calls = [mock_tool_call]
mock_message.model_dump = Mock(
return_value={
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": call_id,
"type": "function",
"function": {
"name": "step",
"arguments": f'{{"action": "{action}"}}',
},
}
],
}
)
mock_completion = AsyncMock()
mock_completion.choices = [Mock(message=mock_message)]
mock_completion.usage = Mock(total_tokens=12)
return mock_completion
# Smart AI for parallel test
winning_sequence = ["right", "right", "down", "down", "down", "right"]
rollout_counter = [0]
def smart_move_generator(**kwargs):
messages = kwargs.get("messages", [])
move_count = sum(1 for msg in messages if msg.get("role") == "assistant" and msg.get("tool_calls"))
rollout_counter[0] += 1
if rollout_counter[0] <= 6: # First rollout wins
action = winning_sequence[move_count] if move_count < len(winning_sequence) else "right"
else: # Second/third rollout makes mistake
action = (
"right"
if move_count == 4
else (winning_sequence[move_count] if move_count < len(winning_sequence) else "right")
)
return create_tool_call_response(action, f"call_{move_count}")
mock_openai_client.chat.completions.create = AsyncMock(side_effect=smart_move_generator)
# Mock Fireworks API response (backup)
mock_response = AsyncMock()
mock_response.status = 200
mock_response.json = AsyncMock(
return_value={
"choices": [
{
"message": {"content": "right", "role": "assistant"},
"finish_reason": "stop",
}
],
"usage": {"total_tokens": 12},
}
)
mock_aiohttp_post.return_value.__aenter__.return_value = mock_response
# Use sophisticated game mock for parallel test
game_mock = self._create_sophisticated_game_mock()
mock_httpx_post.side_effect = game_mock.handle_request
# Mock health check
mock_health_response = Mock()
mock_health_response.status_code = 200
mock_requests_get.return_value = mock_health_response
task_manager = TaskManager()
# Load the frozen lake task definition
task_def_path = Path("examples/frozen_lake/client/task_def.yaml")
if not task_def_path.exists():
pytest.skip(f"Task definition not found: {task_def_path}")
# Load and register the task
task_def = task_manager._load_task_from_file(str(task_def_path))
assert task_def is not None, "Failed to load task definition"
# Test with more rollouts to verify parallelism
task_def.num_rollouts = 3
task_id = task_manager.register_task(task_def)
# Configure subprocess.Popen mock to prevent real process creation
mock_process = Mock()
mock_process.pid = 12347
mock_process.poll.return_value = None
mock_process.communicate.return_value = (b"", b"")
mock_subprocess_popen.return_value = mock_process
try:
# Mock server process management
with (
patch.object(task_manager, "_start_resource_server", return_value=12347),
patch.object(task_manager, "_wait_for_server_health", return_value=True),
):
# Execute with parallel enabled
results = await task_manager.execute_tasks(
task_ids=[task_id],
parallel=True,
max_concurrency=2,
num_rollouts_override=3,
)
# Validate results
assert task_id in results
result = results[task_id]
# Should be successful and aggregated
assert not (isinstance(result, dict) and "error" in result)
assert result.get("aggregated", False)
assert result["num_rollouts"] == 3
logging.info(f"Parallel batch evaluation completed: {result}")
finally:
await task_manager.cleanup()
@pytest.mark.asyncio
@patch("eval_protocol.agent.orchestrator.AsyncOpenAI")
@patch("subprocess.Popen")
@patch("aiohttp.ClientSession.post")
@patch("httpx.Client.post")
@patch("requests.get")
async def test_server_lifecycle_management(
self,
mock_requests_get,
mock_httpx_post,
mock_aiohttp_post,
mock_subprocess_popen,
mock_openai_constructor,
):
"""Test that resource servers are properly started and stopped."""